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WE BUILT THEM, BUT WE DON’T UNDERSTAND THEM

JON KLEINBERG

Tisch University Professor of Computer Science, Cornell University; coauthor (with David Easley), Networks, Crowds, and Markets: Reasoning About a Highly Connected World

SENDHIL MULLAINATHAN

Professor of economics, Harvard University; coauthor (with Eldar Shafir), Scarcity: Why Having Too Little Means So Much

As generations of algorithms get smarter, they’re also becoming more incomprehensible. But to deal with machines that think, we must understand how they think. We have, perhaps for the first time ever, built machines we don’t understand.

We programmed them, so we understand each individual step. But a machine takes billions of these steps and produces behaviors—chess moves, movie recommendations, the sensation of a skilled driver steering through the curves of a road—that aren’t evident from the architecture of the program we wrote.

We’ve made this incomprehensibility easy to overlook. We’ve designed machines to act the way we do: They help drive our cars, fly our airplanes, route our packages, approve our loans, screen our messages, recommend our entertainment, suggest potential romantic partners, and enable our doctors to diagnose what ails us. And because they act like us, it would be reasonable to imagine they think like us, too. But the reality is they don’t think like us at all; at some deep level, we don’t even really understand how they’re producing the behavior we observe. This is the essence of their incomprehensibility.

Does it matter? Should we worry that we’re building systems whose increasingly accurate decisions are based on incomprehensible foundations?

First, and most simply, it matters because we regularly find ourselves in everyday situations where we need to know why. Why was I denied a loan? Why was my account blocked? Why did my condition suddenly get classified as “severe”? And sometimes we need to know why in cases where the machine made a mistake. Why did the self-driving car abruptly go off the road? It’s hard to troubleshoot problems when you don’t understand why they’re happening.

There are deeper troubles, too; to talk about them, we need to understand more about how these algorithms work. They’re trained on massive quantities of data and they’re remarkably good at picking up on the subtle patterns these data contain. We know, for example, how to build systems that can look at millions of identically structured loan applications from the past, all encoded the same way, and start to identify the recurring patterns in the loans that—in retrospect—were the right ones to grant. It’s hard to get human beings to read millions of loan applications, and they wouldn’t do as well as the algorithm even if they did.

This is a genuinely impressive achievement, but a brittle one. The algorithm has a narrow comfort zone in which it can be effective; it’s hard to characterize this comfort zone but easy to step out of it. For example, you might want to move on from the machine’s success classifying millions of small consumer loans and instead give it a database of loan histories from a few thousand complex businesses. But in doing so, you’ve lost the ingredients that make the machine so strong. It draws its power from access to a huge number of data points, a mind-numbingly repetitive history of past instances in which to find patterns and structure. Reduce the amount of data dramatically, or make each data point significantly more complex, and the algorithm quickly starts to flail. Watching the machine’s successes—and they’re phenomenal when conditions are right—is like marveling at the performance of a prodigy whose jaw-dropping achievements and unnerving single-mindedness can mask his or her limitations in other dimensions.

But even in the heart of the machine’s comfort zone, its incomprehensible reasoning leads to difficulties. Take the millions of small consumer loan applications again: Trouble arrives as soon as any of the machine’s customers, managers, or assistants start asking a few simple questions.

A consumer whose loan was denied might ask not just for an explanation but for something more: “How could I change my application next year to have a better chance of success?” Since we don’t have a simple explanation for the algorithm’s decision, there tends not to be a good answer to this question. “Try writing it so it looks more like one of the successful loan applications.” Next question.

An executive might ask, “The algorithm is doing well on loan applications in the United Kingdom. Will it also do well if we deploy it in Brazil?” There’s no satisfying answer here, either; we’re not good at assessing how well a highly optimized rule will transfer to a new domain.

A data scientist might say, “We know how well the algorithm does with the data it has. But surely more information about the consumers would help it. What new data should we collect?” Our human domain knowledge suggests lots of possibilities, but with an incomprehensible algorithm we don’t know which of these possibilities will help it. Think of the irony: We could try picking the variables we ourselves would find useful, but the machine doesn’t think like us and it’s already outperforming us. So how do we know what it will find useful?

This needn’t be the end of the story. We’re starting to see an interest in building algorithms that are not only powerful but also understandable by their creators. To do this, we may need to seriously rethink our notions of comprehensibility. We might never understand, step-by-step, what our automated systems are doing, but that may be OK. It may be enough that we learn to interact with them as one intelligent entity interacts with another, developing a robust sense for when to trust their recommendations, where to employ them most effectively, and how to help them reach a level of success we’d never achieve on our own.

Until then, however, the incomprehensibility of these systems creates a risk. How do we know when the machine has left its comfort zone and is operating on parts of the problem it’s not good at? The extent of this risk isn’t easy to quantify, and it’s something we must confront as our systems develop. We may eventually have to worry about all-powerful machine intelligence. But first we need to worry about putting machines in charge of decisions they don’t have the intelligence to make.

 
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